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Abstract

In the last few decades, considerable effort has been devoted to the phenomenon of wave-induced liquefaction. In deed, it is one of the most important factors used in analysing
the seabed stability and in designing marine structures. As waves propagate and fluctuate
over the ocean surface, energy is carried within the medium of the water particles. When
this energy is transmitted into the seabed, the results are a rather complex mechanism of
soil behaviours that significantly affect the stability of the seabed.
The prediction of wave-induced seabed liquefaction has been recognised by coastal
geotechnical engineers as an ...View more >In the last few decades, considerable effort has been devoted to the phenomenon of wave-induced liquefaction. In deed, it is one of the most important factors used in analysing
the seabed stability and in designing marine structures. As waves propagate and fluctuate
over the ocean surface, energy is carried within the medium of the water particles. When
this energy is transmitted into the seabed, the results are a rather complex mechanism of
soil behaviours that significantly affect the stability of the seabed.
The prediction of wave-induced seabed liquefaction has been recognised by coastal
geotechnical engineers as an important factor when considering the design of marine
structures. All existing prediction of wave-induced seabed liquefaction models have been
based on conventional approaches of engineering mechanics, with limited laboratory work.
Previous studies have involved complicated procedures and complex mathematical methods.
The present meticulous study has been based on the existing poro-elastic wave-induced
seabed liquefaction solution, and has adopted Artificial Intelligence (AI) technology to
predict maximum wave-induced seabed liquefaction. The author has proposed an alternative
approach for prediction of the maximum liquefaction depth, based on the Artificial
Neural Network (ANN). Unlike previous engineering mechanical approaches, the various
proposed ANN models are based on data learning knowledge, rather than on the knowledge
of the mechanisms. The author has concluded that ANN models can be applicable
to such engineering exercise at least this study.View less >